Data Engineer Training
Toronto, Canada

We offer proven Data Engineer Training
regularly delivered to our Fortune 500 clients
around the world.

Data Engineer Training Courses and Schedules

Data Engineer Training Fundamentals

Azure Data Engineer Training

AWS Data Engineer Training

Data Engineering with Python

In this Data Engineer Training Video, we’ll review the core capabilities of Python that enable developers to solve a variety of data engineering problems. We’ll also review NumPy and pandas libraries, with a focus on such topics as the need for understanding your data, selecting the right data types, improving performance of your applications, common data repairing techniques, and so on.

Related course: Data Engineering with Python (WA2905)

Proven Results

For over 20 years, we have trained thousands of developers at some of the country’s largest tech companies – including many Fortune 500 companies. Here are a few of the clients we have delivered Data Engineering Courses to:

Reviews from past students who completed our Data Engineering Courses:

“This was a great course. I loved the blend of Python Concepts Plus Just enough Data science to be productive”

“Instructor was very thorough, yet practical. He was a great communicator and explained everything in layman’s terms.”

“Great tutorials! I will go back to these”

“This course is excellent. It gave me an overview of data science and a good understanding. It put me in the right direction of data analysis in my work.”

Should I Attend Data Engineer Training Toronto?

The data engineering field is expected to continue growing rapidly over the next several years, and there’s huge demand for data engineers across industries.

The global Big Data and data engineering services market is expected to grow at a CAGR of 31.3 percent by 2025.

Comprehensive data engineer training with Web Age will teach you to organize, analyze and interpret your many sources of big data and information.

The data engineer training courses we offer provide your teams the skills they need to discover insights required for analyzing and tackling critical factors such as risk, performance, quality, forecasting, estimating, simulation, business process improvement, and much more.

Data Engineer Training Toronto!

A global city, Toronto is a center of business, finance, arts, and culture, and is recognized as one of the most multicultural and cosmopolitan cities in the world.

Toronto is quickly becoming one of the largest tech hubs in Canada, supporting the vision city officials have of Toronto as the next ‘Silicon Valley North’. Working in Toronto is a great career choice – especially in the IT and communications sectors.

Web Age Data Engineering Courses are taught at locations throughout Ontario and Quebec venues. Our Toronto Data Engineer Training classes are delivered in traditional classroom style format. Online Data Engineering courses are also available in a synchronous instructor led format.

PySpark for Data Engineering & Machine Learning

In this Data Engineer Training video we will review the core capabilities of PySpark as well as PySpark’s areas of specialization in data engineering, ETL, and Machine Learning use cases.

Data Engineering with Python Training (WA2905)

What is a Data Engineer?

A data engineer conceives, builds and maintains the data infrastructure that holds your enterprise’s advanced analytics capacities together.

A data engineer is responsible for building and maintaining the data architecture of a data science project. Data Engineers are responsible for the creation and maintenance of analytics infrastructure that enables almost every other function in the data world. They are responsible for the development, construction, maintenance and testing of architectures, such as databases and large-scale processing systems. As part of this, Data Engineers are also responsible for the creation of data set processes used in modeling, mining, acquisition, and verification.

What is Data Engineering

Data engineering is a software engineering practice with a focus on design, development, and productionizing of data processing systems. Data processing includes all the practical aspects of data handling, including:Data acquisition, transfer, transformation, and storage on-prem or in the cloud. In many cases, data can be categorized as Big Data.

Gartner’s Definition of Big Data

Gartner’s analyst Doug Laney defined three dimensions to data growth challenges: increasing volume (amount of data), velocity (speed of data in and out), and variety (range of data types and sources).

In 2012, Gartner updated its definition as follows: “Big data are high volume, high velocity, and/or high variety information assets that require new forms of processing to enable enhanced decision making, insight discovery and process optimization.”

Volume

Data sizes accumulated in many organizations come to hundreds of terabytes, approaching the petabyte levels.

Variety

Big Data comes in different formats as well as unformatted (unstructured) and various types like text, audio, voice, VoIP, images, video, e-mails, web traffic log files entries, sensor byte streams, etc.

Velocity

High traffic on-line banking web site can generate hundreds of TPS (transactions per second) each of which may be required to be subjected to fraud detection analysis in real or near-real time.

What is the difference between a Data Scientist and a Data Engineer?

Broadly speaking, a data scientist builds models using a combination of statistics, mathematics, machine learning and domain based knowledge. He/she has to code and build these models using the same tools/languages and framework that the organization supports.

A data engineer on the other hand has to build and maintain data structures and architectures for data ingestion, processing, and deployment for large-scale data-intensive applications. To build a pipeline for data collection and storage, to funnel the data to the data scientists, to put the model into production – these are just some of the tasks a data engineer has to perform.

Data scientists and data engineers need to work together for any large scale data science project to suc

What are the core Data Engineering skills?

Introduction to Data Engineering (Related Course: Data Engineering Training for Managers)
Basic Language Requirement: Python (Related Course: Introduction to Python Programming)
Solid Knowledge of Operating Systems (Related Courses: Unix Training)
Heavy, In-Depth Database Knowledge – SQL and NoSQL (Related Courses: NoSQL Training and Courseware)
Data Warehousing – Hadoop, MapReduce, HIVE, PIG, Apache Spark, Kafka (Related Courses: Big Data Training)
Basic Machine Learning Familiarity (Related Courses: AI and Machine Learning Training)